{"title":"大数据分析软件定义弹性系统原理","authors":"Hong Linh Truong, S. Dustdar","doi":"10.1109/IC2E.2014.67","DOIUrl":null,"url":null,"abstract":"Techniques for big data analytics should support principles of elasticity that are inherent in types of data and data resources being analyzed, computational models and computing units used for analyzing data, and the quality of results expected from the consumer. In this paper, we analyze and present these principles and their consequences for software-defined environments to support data analytics. We will conceptualize software-defined elastic systems for data analytics and present a case study in smart city management, urban mobility and energy systems with our elasticity supports.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Principles of Software-Defined Elastic Systems for Big Data Analytics\",\"authors\":\"Hong Linh Truong, S. Dustdar\",\"doi\":\"10.1109/IC2E.2014.67\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Techniques for big data analytics should support principles of elasticity that are inherent in types of data and data resources being analyzed, computational models and computing units used for analyzing data, and the quality of results expected from the consumer. In this paper, we analyze and present these principles and their consequences for software-defined environments to support data analytics. We will conceptualize software-defined elastic systems for data analytics and present a case study in smart city management, urban mobility and energy systems with our elasticity supports.\",\"PeriodicalId\":273902,\"journal\":{\"name\":\"2014 IEEE International Conference on Cloud Engineering\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Cloud Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC2E.2014.67\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Cloud Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC2E.2014.67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Principles of Software-Defined Elastic Systems for Big Data Analytics
Techniques for big data analytics should support principles of elasticity that are inherent in types of data and data resources being analyzed, computational models and computing units used for analyzing data, and the quality of results expected from the consumer. In this paper, we analyze and present these principles and their consequences for software-defined environments to support data analytics. We will conceptualize software-defined elastic systems for data analytics and present a case study in smart city management, urban mobility and energy systems with our elasticity supports.
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